Defective workpiece image recognition method based on convolutional attention neural network

Abstract

The invention discloses a defective workpiece image recognition method based on a convolutional attention neural network. The method comprises the steps of performing pre-training weight on the convolutional neural network to form a deep feature for input image input, of a feature extraction network; carrying out convolution operation on the depth feature image to obtain an attention graph, and performing normalization; carrying out attention region cutting and discarding on the normalized attention map, and inputting the attention region cutting and discarding into the convolutional neural network again for training; inputting a defective workpiece image to be recognized into the trained convolutional neural network to obtain a feature map, and performing dot product operation on the feature map and the attention map to obtain a new part feature map; performing maximum pooling operation on the part feature map to obtain attention features, stacking all the attention features to obtaina part feature matrix, and completing classification and recognition of defect images. According to the invention, the accuracy of workpiece defect detection is improved, and the method can be applied to various tiny defect detection tasks.

Description

technical field [0001] The invention relates to a defect workpiece image recognition method, in particular to a defect workpiece image recognition method based on a convolutional attention neural network. Background technique [0002] There may be a variety of defects in workpiece casting production. The defects on the surface of the workpiece can be judged by direct observation, but some defects exist inside the workpiece, which cannot be judged by observation. X-ray images are often used for non-destructive testing. The discrimination of X-ray images has been done manually in the past, and the discrimination efficiency is low. With the development of artificial intelligence, especially the development of algorithms such as machine learning, the application of computers for defect discrimination has improved efficiency. However, the machine learning algorithm also has the problem of poor versatility. It is necessary to build a corresponding model for a workpiece, and the s...

AI Technical Summary

Problems solved by technology

[0004] Aiming at the defects of the above-mentioned prior art, the present invention provides a defect workpiece image recognition method based on a convolutional attention neural network, which solves the problem of low recognition rate caused by small defects being easily overlooked

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